Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors

doi:10.1145/3448416

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors
	Liang, Yunji 1; Wang, Xin2 ; Yu, Zhiwen 1; Guo, Bin 1; Zheng, Xiaolong3 ; Samtani, Sagar 4
发表期刊	ACM TRANSACTIONS ON SENSOR NETWORKS
ISSN	1550-4859
	2021-08-01
卷号	17 期号:3 页码:28
通讯作者	Liang, Yunji(liangyunji@nwpu.edu.cn)
摘要	With the proliferation of Internet of Things (IoT) devices in the consumer market, the unprecedented sensing capability of IoT devices makes it possible to develop advanced sensing and complex inference tasks by leveraging heterogeneous sensors embedded in IoT devices. However, the limited power supply and the restricted computation capability make it challenging to conduct seamless sensing and continuous inference tasks on resource-constrained devices. How to conduct energy-efficient sensing and perform rich-sensor inference tasks on IoT devices is crucial for the success of IoT applications. Therefore, we propose a novel energyefficient collaborative sensing framework to optimize the energy consumption of IoT devices. Specifically, we explore the latent correlations among heterogeneous sensors via an attention mechanism in temporal convolutional network to quantify the dependency among sensors, and characterize the heterogeneous sensors in terms of energy consumption to categorize them into low-power sensors and energy-intensive sensors. Finally, to decrease the sampling frequency of energy-intensive sensors, we propose a multi-task learning strategy to predict the statuses of energy-intensive sensors based on the low-power sensors. To evaluate the performance of the proposed collaborative sensing framework, we develop a mobile application to collect concurrent heterogeneous data streams from all sensors embedded in Huawei Mate 8. The experimental results show that latent correlation learning is greatly helpful to understand the latent correlations among heterogeneous streams, and it is feasible to predict the statuses of energy-intensive sensors by low-power sensors with high accuracy and fast convergence. In terms of energy consumption, the proposed collaborative sensing framework is able to preserve the energy consumption of IoT devices by nearly 50% for continuous data acquisition tasks.
关键词	Energy efficiency latent correlation learning collaboration sensing internet of things temporal convolutional network attention mechanism multi-task learning
DOI	10.1145/3448416
关键词[WOS]	CONTINUOUS AUTHENTICATION ; INTELLIGENCE ; INDUSTRY ; INTERNET ; DEVICES
收录类别	SCI
语种	英语
资助项目	National Major Program for Technological Innovation[2018AAA0100500] ; natural science foundation of China[61902320] ; fundamental research funds for the central universities[31020180QD140]
项目资助者	National Major Program for Technological Innovation ; natural science foundation of China ; fundamental research funds for the central universities
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:000777801400013
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48326
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Liang, Yunji
作者单位	1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China 2.Northwestern Polytech Univ, Sch Software, Xian 710129, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Indiana Univ, Kelley Sch Business, Bloomington, IN 47405 USA
推荐引用方式 GB/T 7714	Liang, Yunji,Wang, Xin,Yu, Zhiwen,et al. Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors[J]. ACM TRANSACTIONS ON SENSOR NETWORKS,2021,17(3):28.
APA	Liang, Yunji,Wang, Xin,Yu, Zhiwen,Guo, Bin,Zheng, Xiaolong,&Samtani, Sagar.(2021).Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors.ACM TRANSACTIONS ON SENSOR NETWORKS,17(3),28.
MLA	Liang, Yunji,et al."Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors".ACM TRANSACTIONS ON SENSOR NETWORKS 17.3(2021):28.